The Hermite Moment Model for Highly Skewed Response With Application to Tension Leg Platforms

Choi, Mi‐Jin; Sweetman, Bert

doi:10.1115/1.4000398

Cited by 48 publications

(12 citation statements)

References 11 publications

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“…Several solutions have been proposed to deal with the pressure coefficients falling outside the monotone region. Choi (Choi and Sweetman, 2010) proposes to overcome this limit by taking a softening-hardening-softening transformation in order to keep an increasing (monotone) transformation xðuÞ and cross the decreasing part of the softening process. This technique requires making a hybrid model depending on the value of x and deform the nature of the process.…”

Section: Required Duration Of the Time Seriesmentioning

confidence: 99%

A de-mixing approach for the management of large negative peaks in wind tunnel data

Rigo

Andrianne

Denoël

2020

Journal of Wind Engineering and Industrial Aerodynamics

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Large negative peak pressures commonly take place near the edges of buildings due to the presence of local corner vortices and impingement of shear layers. As a result, Probability Density Functions (PDF) of the measured pressure signals exhibit one or more components which contributes to the non-Gaussianity of the pressure loading. These mixed flows can be modeled with mixture models. Whenever several processes coexist, and when one of them is leading in the tail of the statistical distribution, it is natural to construct the extreme value model with only this process leading in the tail and not with the mixed observed pressures. In this paper, we propose a method that is based on the autocorrelation of the pressure coefficient to de-mix the measured signals. This information improves the de-mixing process where classical methods would struggle. Indeed, the two phenomena to be separated and identified might be characterized by significantly different timescales , which are not reflected in the PDF. In this paper, the large negative pressures measured on a flat roof are analyzed and decomposed into two elementary processes, namely, the flapping corner vortex and the turbulent flow detaching from the sharp upstream edges. This paper finally shows that an accurate decomposition of the recorded pressures into their underlying modes provides a more meaningful evaluation of extreme pressures.

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Section: Required Duration Of the Time Seriesmentioning

confidence: 99%

A de-mixing approach for the management of large negative peaks in wind tunnel data

Rigo

Andrianne

Denoël

2020

Journal of Wind Engineering and Industrial Aerodynamics

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“…These taps a represented on the roof in Figure 9(b), precisely in the corner vortices region. Choi [18] studied the different regions and monotone limits of Hermite-based model (for softening and hardening processes). Strong negative wind pressures measured on buildings and roofs are mostly softening processes.…”

Section: Cubic Translation and Peak Factor Estimationmentioning

confidence: 99%

“…In our case, taps falling outside the monotone limit are in region III of Figure 10(a). Choi [18] proposes to overcome this limit by taking a softening-hardening-softening transformation (Figure 10(b)) in order to keep an increasing (monotone) transformation and cross the decreasing part of the softening process. This technique requires a hybrid model depending on the value of and deform the nature of the process.…”

Section: Cubic Translation and Peak Factor Estimationmentioning

confidence: 99%

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On the Use of the Cubic Translation to Model Bimodal Wind Pressures

Rigo

Andrianne

Denoël

2019

Mathematical Modelling in Civil Engineering

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The cubic translation model is a well know tool in wind engineering, which provides a mathematical description of a non-Gaussian pressure as a cubic transformation of a Gaussian process. This simple model is widely used in practice since it offers a direct evaluation of the peak factors as a function of the statistics of the wind pressure data. This transformation is rather versatile but limited to processes which are said to be in the monotonic region. For processes falling outside this domain, this paper describes an alternative which is based on the physics of the wind flow. First, it is shown, with a classical example of a flow involving corner vortices on a flat roof, that the pressure data which does not meet the monotonic criterion is in fact associated with a bimodal distribution. Then, the proposed approach is to decompose this data into the two governing modes (slow background turbulence and fast corner vortices) and apply the usual translation model to each of them.

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“…Winterstein’s polynomials offers equivalent Gaussian fractile mapping between Gaussian and non-Gaussian processes and has been widely used (Jensen, 1994; Puig and Akian, 2004; Winterstein and Kashef, 2000; Winterstein and Mackenzie, 2011). Winterstein’s polynomials, however, cannot be directly applied to some processes (Choi and Sweetman, 2010) since the transformation is non-monotonic for certain combinations of skewness and kurtosis (Low, 2013) and is known to have poor accuracy for strongly non-Gaussian processes (Ding and Chen, 2014). Based on Winterstein’s polynomials, the first passage probability for stationary non-Gaussian response with the consideration of the effects of clumping and initial conditions on the up-crossing rate has been proposed by He and Zhao (2007).…”

Section: Introductionmentioning

confidence: 99%

First passage probability assessment of stationary non-Gaussian process using the third-order polynomial transformation

Zhao

Zhang

et al. 2018

Advances in Structural Engineering

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In this article, an analytical moment-based procedure is developed for estimating the first passage probability of stationary non-Gaussian structural responses for practical applications. In the procedure, an improved explicit third-order polynomial transformation (fourth-moment Gaussian transformation) is proposed, and the coefficients of the third-order polynomial transformation are first determined by the first four moments (i.e. mean, standard deviation, skewness, and kurtosis) of the structural response. The inverse transformation (the equivalent Gaussian fractile) of the third-order polynomial transformation is then used to map the marginal distributions of a non-Gaussian response into the standard Gaussian distributions. Finally, the first passage probabilities can be calculated with the consideration of the effects of clumping crossings and initial conditions. The accuracy and efficiency of the proposed transformation are demonstrated through several numerical examples for both the “softening” responses (with wider tails than Gaussian distribution; for example, kurtosis > 3) and “hardening” responses (with narrower tails; for example, kurtosis < 3). It is found that the proposed method has better accuracy for estimating the first passage probabilities than the existing methods, which provides an efficient and rational tool for the first passage probability assessment of stationary non-Gaussian process.

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The Hermite Moment Model for Highly Skewed Response With Application to Tension Leg Platforms

Cited by 48 publications

References 11 publications

A de-mixing approach for the management of large negative peaks in wind tunnel data

A de-mixing approach for the management of large negative peaks in wind tunnel data

On the Use of the Cubic Translation to Model Bimodal Wind Pressures

First passage probability assessment of stationary non-Gaussian process using the third-order polynomial transformation

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